abstract: The exponential rise in unmanned aerial vehicles has necessitated the need for accurate pose estimation under any extreme conditions. Visual Odometry (VO) is the estimation of position and orientation of a vehicle based on analysis of a sequence of images captured from a camera mounted on it. VO offers a cheap and relatively accurate alternative to conventional odometry techniques like wheel odometry, inertial measurement systems and global positioning system (GPS). This thesis implements and analyzes the performance of a two camera based VO called Stereo based visual odometry (SVO) in presence of various deterrent factors like shadows, extremely bright outdoors, wet conditions etc... To allow the implementation of VO on any generic vehicle, a discussion on porting of the VO algorithm to android handsets is presented too. The SVO is implemented in three steps. In the first step, a dense disparity map for a scene is computed. To achieve this we utilize sum of absolute differences technique for stereo matching on rectified and pre-filtered stereo frames. Epipolar geometry is used to simplify the matching problem. The second step involves feature detection and temporal matching. Feature detection is carried out by Harris corner detector. These features are matched between two consecutive frames using the Lucas-Kanade feature tracker. The 3D co-ordinates of these matched set of features are computed from the disparity map obtained from the first step and are mapped into each other by a translation and a rotation. The rotation and translation is computed using least squares minimization with the aid of Singular Value Decomposition. Random Sample Consensus (RANSAC) is used for outlier detection. This comprises the third step. The accuracy of the algorithm is quantified based on the final position error, which is the difference between the final position computed by the SVO algorithm and the final ground truth position as obtained from the GPS. The SVO showed an error of around 1% under normal conditions for a path length of 60 m and around 3% in bright conditions for a path length of 130 m. The algorithm suffered in presence of shadows and vibrations, with errors of around 15% and path lengths of 20 m and 100 m respectively. / Dissertation/Thesis / M.S. Electrical Engineering 2010

No autorizada por el autor a ser publicada a texto completo / La presente memoria se realizó en la empresa LAN Airlines S.A. Su objetivo es determinar una metodología que permita encontrar el nivel óptimo de reservas en exceso de la capacidad física del avión, con el fin de asegurar una ganancia al evitar que queden asientos vacíos en el vuelo, debido al impredecible comportamiento de pasajeros que no se presentan al embarque (no-show). Esta práctica se denomina sobreventa u overbooking. Hoy en día la Compañía incurre en costos monetarios y de oportunidad innecesarios debidos a la mala estimación de la sobreventa. Para lograr el objetivo se trabajó con el sistema de Revenue Management PROS O&D, el cuál utiliza parámetros de optimización de sobreventa tal que se minimicen los costos asociados. Estos son el costo de Denied Boarding (DB), asociado al costo de negar el acceso a abordar a un pasajero; y costo Spoilage, ingreso que se deja de percibir por cada asiento vacío que queda en un vuelo, cuando existía demanda. Se realizó un extenso análisis de bases de datos para identificar y determinar el valor adecuado para cada uno de los costos y parámetros. Para el costo de DB se analizaron las compensaciones de cada Unidad de Negocio y se realizaron regresiones que permitieron obtener tramos de DB asociados a un mismo costo, junto con el factor en que aumenta el costo de cada tramo. Se determinó el costo Spoilage como el ingreso promedio de la cabina Economy para vuelos con factor de ocupación superior a 90%. Se definieron cotas máximas de overbooking según temporada, en base a promedios históricos de pasajeros no-show. Además, se determinó el número máximo de pasajeros DB a aceptar por vuelo. Y se escogió un porcentaje adecuado para representar a los pasajeros DB voluntarios considerados, éstos corresponden a los que aceptan la compensación que ofrece la Compañía. Se analizaron 2 vuelos Nacionales y 2 Internacionales, durante noviembre del 2010. Con el valor real de pasajeros no-show, se calculó un overbooking de equilibrio en el cual no se incurre en un costo de Spoilage ni de DB, con esto se compararon los resultados para la metodología propuesta y para los valores actualmente cargados. Para vuelos Internacionales se disminuye el costo de DB y se aumenta el costo de Spoilage, lo que se traduce en una disminución del costo total mensual en USD 86.968 y USD 24.874, para cada vuelo. En el caso Nacional, aumentó el costo de DB y disminuyó el Spoilage, lo que se tradujo en un ahorro en costos de USD 4.296. El otro vuelo, aumentó los costos en USD 24.300, lo que llevó a realizar un análisis de los pronósticos de no-show utilizados por PROS, permitiendo detectar que el mal pronóstico utilizado para este vuelo en particular podría justificar el mal resultado. La metodología propuesta logró mejorar las estimaciones de overbooking y a la vez disminuir los costos asociados a la sobreventa. Sin embargo, es necesario corroborar que los pronósticos utilizados por PROS sean los adecuados para que éstos no afecten negativamente a la optimización.

No description available.

The use of multirotor UAVs to map GPS-degraded environments is useful for many purposes ranging from routine structural inspections to post-disaster exploration to search for survivors and evaluate structural integrity. Multirotor UAVs are able to reach many areas that humans and other robots cannot safely access. Because of their relatively short operational flight time compared to other robotic applications, using multiple UAVs to collaboratively map these environments can streamline the mapping process significantly. This research focuses on four primary areas regarding autonomous mapping and navigation with multiple UAVs in complex unknown or partially unknown GPS-denied environments: The first area is the high-level coverage path planning necessary to successfully map these environments with multiple agents. The second area is the lower-level reactive path planning that enables autonomous navigation through complex, unknown environments. Third, is the estimation framework that enables autonomous flight without the use of GPS or other global position sensors. Lastly, it focuses on the mapping framework to build a single dense 3D map of these environments with multiple agents flying simultaneously.

Can an outward facing camera on a bus, be used to recognize its location in GPS denied environment? Observit, provides cloud-based mobile surveillance services for bus operators using IP cameras with wireless connectivity. With the continuous gathering of video information, it opens up new possibilities for additional services. One service is to use the information with the technology, visual place recognition, to locate the vehicle, where the image was taken. The objective of this thesis has been to answer, how well can learnable visual place recognition methods localize a bus in a GPS denied environment and if a lightweight model can achieve the same accurate results as a heavyweight model. In order to achieve this, four model architecture has been implemented, trained and evaluate on a created dataset of interesting places. A visual place recognition application has been implemented as well, in order to test the models on bus video footage. The results show that the heavyweight model constructed of VGG16 with Patch-NetVLAD, performed best on the task with different recall@N values and got a recall@1 score of 92.31%. The lightweight model that used the backbone of MobileNetV2 with Patch-NetVLAD, scored similar recall@N results as the heavyweight model and got the same recall@1 score. The thesis shows that, with different localization methods, it is possible for a vehicle to identify its position in a GPS denied environment, with a model that could be deploy on a camera. This work, impacts companies that rely on cameras as their source of service.

Autonomous flight in unstructured, confined, and unknown GPS-denied environments is a challenging problem. Solutions could be tremendously beneficial for scenarios that require information about areas that are difficult to access and that present a great amount of risk. The goal of this research is to develop a new framework that enables improved solutions to this problem and to validate the approach with experiments using a hardware prototype. In Chapter 2 we examine the consequences and practical aspects of using an improved dynamic model for multirotor state estimation, using only IMU measurements. The improved model correctly explains the measurements available from the accelerometers on a multirotor. We provide hardware results demonstrating the improved attitude, velocity and even position estimates that can be achieved through the use of this model. We propose a new architecture to simplify some of the challenges that constrain GPS-denied aerial flight in Chapter 3. At the core, the approach combines visual graph-SLAM with a multiplicative extended Kalman filter (MEKF). More importantly, we depart from the common practice of estimating global states and instead keep the position and yaw states of the MEKF relative to the current node in the map. This relative navigation approach provides a tremendous benefit compared to maintaining estimates with respect to a single global coordinate frame. We discuss the architecture of this new system and provide important details for each component. We verify the approach with goal-directed autonomous flight-test results. The MEKF is the basis of the new relative navigation approach and is detailed in Chapter 4. We derive the relative filter and show how the states must be augmented and marginalized each time a new node is declared. The relative estimation approach is verified using hardware flight test results accompanied by comparisons to motion capture truth. Additionally, flight results with estimates in the control loop are provided. We believe that the relative, vision-based framework described in this work is an important step in furthering the capabilities of indoor aerial navigation in confined, unknown environments. Current approaches incur challenging problems by requiring globally referenced states. Utilizinga relative approach allows more flexibility as the critical, real-time processes of localization and control do not depend on computationally-demanding optimization and loop-closure processes.

Prior to the wide deployment of robotic systems, they must be able to navigate autonomously. These systems cannot rely on good weather or daytime navigation and they must also be able to navigate in unknown environments. All of this must take place without human interaction. A majority of modern autonomous systems rely on GPS for position estimation. While GPS solutions are readily available, GPS is often lost and may even be jammed. To this end, a significant amount of research has focused on GPS-denied navigation. Many GPS-denied solutions rely on known environmental features for navigation. Others use vision sensors, which often perform poorly at high altitudes and are limited in poor weather. In contrast, radar systems accurately measure range at high and low altitudes. Additionally, these systems remain unaffected by inclimate weather. This dissertation develops the use of radar odometry for GPS-denied navigation. Using the range progression of unknown environmental features, the aircraft's motion is estimated. Results are presented for both simulated and real radar data. In Chapter 2 a greedy radar odometry algorithm is presented. It uses the Hough transform to identify the range progression of ground point-scatterers. A global nearest neighbor approach is implemented to perform data association. Assuming a piece-wise constant heading assumption, as the aircraft passes pairs of scatterers, the location of the scatterers are triangulated, and the motion of the aircraft is estimated. Real flight data is used to validate the approach. Simulated flight data explores the robustness of the approach when the heading assumption is violated. Chapter 3 explores a more robust radar odometry technique, where the relatively constant heading assumption is removed. This chapter uses the recursive-random sample consensus (R-RANSAC) Algorithm to identify, associate, and track the point scatterers. Using the measured ranges to the tracked scatterers, an extended Kalman filter (EKF) iteratively estimates the aircraft's position in addition to the relative locations of each reflector. Real flight data is used to validate the accuracy of this approach. Chapter 4 performs observability analysis of a range-only sensor. An observable, radar odometry approach is proposed. It improves the previous approaches by adding a more robust R-RANSAC above ground level (AGL) tracking algorithm to further improve the navigational accuracy. Real flight results are presented, comparing this approach to the techniques presented in previous chapters.

In this thesis we explore an infrared light-based approach to the problem of pose estimation during aircraft landing in urban environments where GPS is unreliable or unavailable. We introduce a novel fiducial constellation composed of sparse infrared lights that incorporates projective invariant properties in its design to allow for robust recognition and association from arbitrary camera perspectives. We propose a pose estimation pipeline capable of producing high accuracy pose measurements at real-time rates from monocular infrared camera views of the fiducial constellation, and present as part of that pipeline a data association method that is able to robustly identify and associate individual constellation points in the presence of clutter and occlusions. We demonstrate the accuracy and efficiency of this pose estimation approach on real-world data obtained from multiple flight tests, and show that we can obtain decimeter level accuracy from distances of over 100 m from the constellation. To achieve greater robustness to the potentially large number of outlier infrared detections that can arise in urban environments, we also explore learning-based approaches to the outlier rejection and data association problems. By formulating the problem of camera image data association as a 2D point cloud analysis, we can apply deep learning methods designed for 3D point cloud segmentation to achieve robust, high-accuracy associations at constant real-time speeds on infrared images with high outlier-to-inlier ratios. We again demonstrate the efficiency of our learning-based approach on both synthetic and real-world data, and compare the results and limitations of this method to our first-principles-based data association approach.

This thesis is focused on the study of the best-known network attacks, especially on those that can be theoretically detected without knowledge of the contents of transmitted messages. The goal is to use the basis of acquired knowledge to create a tool that will simulate the behavior of the communication in different network attacks. Simulation outputs will be used for testing the quality of security tools designed to defend against network attacks. The simulator will be used only for offline testing, it will not be possible to carry out real attacks. Purpose of this work is to improve the security against network attacks nowadays.

abstract: Robotic swarms can potentially perform complicated tasks such as exploration and mapping at large space and time scales in a parallel and robust fashion. This thesis presents strategies for mapping environmental features of interest – specifically obstacles, collision-free paths, generating a metric map and estimating scalar density fields– in an unknown domain using data obtained by a swarm of resource-constrained robots. First, an approach was developed for mapping a single obstacle using a swarm of point-mass robots with both directed and random motion. The swarm population dynamics are modeled by a set of advection-diffusion-reaction partial differential equations (PDEs) in which a spatially-dependent indicator function marks the presence or absence of the obstacle in the domain. The indicator function is estimated by solving an optimization problem with PDEs as constraints. Second, a methodology for constructing a topological map of an unknown environment was proposed, which indicates collision-free paths for navigation, from data collected by a swarm of finite-sized robots. As an initial step, the number of topological features in the domain was quantified by applying tools from algebraic topology, to a probability function over the explored region that indicates the presence of obstacles. A topological map of the domain is then generated using a graph-based wave propagation algorithm. This approach is further extended, enabling the technique to construct a metric map of an unknown domain with obstacles using uncertain position data collected by a swarm of resource-constrained robots, filtered using intensity measurements of an external signal. Next, a distributed method was developed to construct the occupancy grid map of an unknown environment using a swarm of inexpensive robots or mobile sensors with limited communication. In addition to this, an exploration strategy which combines information theoretic ideas with Levy walks was also proposed. Finally, the problem of reconstructing a two-dimensional scalar field using observations from a subset of a sensor network in which each node communicates its local measurements to its neighboring nodes was addressed. This problem reduces to estimating the initial condition of a large interconnected system with first-order linear dynamics, which can be solved as an optimization problem. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2018